Computer Fails As Job-Killer

EVER SINCE computers were first installed in offices and factories, workers have feared that they were in danger of being replaced. Now there are worries that computers aren't replacing humans fast enough.

That is the chief frustration facing companies that were hoping that the rise of ''expert systems'' - the software equivalent of a doctor, mechanic or stockbroker, among others - would vastly cut labor costs. But as many in the computer industry are increasingly conceding, it hasn't worked out that way. At best, such programs appear to be relieving workers of rote tasks and simple diagnoses. True expertise, it turns out, is a subtle phenomenon - and one that rarely can be replicated in the pre-programmed ''rules'' that enable a software to simulate the thinking of its creators.

''In all the use of expert systems I have ever seen,'' said Bill Turpin, the manager of artificial-intelligence software at Texas Instrument's data systems group, ''I have never seen one yet that could totally replace somebody. And I guess there is a lesson in that.'' A Demystification Process

The lesson is one of many in the recent and rapid demystification of artificial intelligence - both for those who design computers and those who use them. Just a few years ago, the phrase ''expert system'' conjured up images of programs that could make computers reason, converse and learn like the humans whose knowledge they seek to replicate. More than a few struggling software companies nurtured that image: programs that could ''think,'' even in a limited way, were always just a few years away from becoming a commercial product.

Indeed, a handful of such programs, with rudimentary reasoning skills, are now in use. The Ford Motor Company has a system that diagnoses problems with its factory robots, saving engineers the need to leaf through hundreds of pages of reference manuals to find and fix a problem. Wall Street investment houses are toying with systems that evaluate investing opportunities, and companies like Texas Instruments and General Motors are tinkering with systems that advise executives about the right time to buy large equipment or build a new manufacturing facility.

But almost without exception, such programs have proved useful to a rather limited audience - and sometimes they hardly seemed worth the investment. And a whole raft of efforts to simulate other experts' duties - from loan officers at banks to chemists at multinationals - have largely failed. Now the whole field is undergoing re-examination, and more than a few expert-system experts are questioning whether their techniques have been applied to the right problems.

''In the future artificial intelligence should simulate easily replaceable people,'' said Roger C. Schank, a professor of computer science and psychology at Yale University who also heads a fledgling artificial-intelligence company, Cognitive Systems. ''For example, there are a lot of people in the customer-service area - bank tellers, travel agents, airline reservation clerks - who don't do their job very well.'' And they are the kind of people who, programmers hope, they will soon learn to simulate.

The first step in that process came with computers that contain almost no intelligence at all: the bank-teller machine. The machines follow a preset order of instructions, asking the user first for a bank card, then for a password, and then to select from a limited menu of choices - a withdrawal or a deposit, for example, or maybe an account balance.

But the true banking ''expert'' - replicating a human bank officer - could advise a customer how much to keep in savings and how much in checking to get the maximum interest. It might be able to give a quick evaluation of the customer's likelihood to qualify for a mortgage. Or it could sell stocks and insurance.

Actually writing such a program, computer experts note, would not be all that difficult. Most of the decisions made by banks are strictly ''rule-based,'' the industry's lingo for actions that follow a closely prescribed set of guidelines. And rules are the lifeblood of any expert system. Usually, they are expressed as a series of ''if-then'' statements: If the patient complains of stomachaches, then what has she eaten? If the robot has stopped operating, then check its power supply.

But rules, it is turning out, are not enough. What makes ''experts'' is experience, and experience has more to do with past examples than with strict rules.

''A library of cases should be what characterizes the intelligence of a machine,'' contends Professor Schank, who like many in the field questions whether the current generation of expert systems has very much to do with the goal of creating artificial intelligence. ''This concept that rules are where it's at in simulated intelligence is just dead wrong.''

Professor Schank's image of the perfect expert system seems to have more in common with Dear Abby than with HAL, the all-knowing computer in the movie ''2001.'' A customer might ask ''Should I invest in this product?'' The computer wouldn't answer ''yes'' or ''no'' - what investment adviser gives a straight answer? - but rather would offer five cases in the history of corporate America that bear similarities to the one at hand, and instruct the customer to ''see which ones are relevant.''

In the course of a dialogue the machine could refine its analysis, drawing on more and more information supplied by the customer. And ideally, it would track the investment in the company in question, adding the results automatically to its data bank of knowledge.

Such Socratic exchanges are already taking place, in a limited way, in laboratory experiments. Airlines are toying with systems that tell potential customers not only about flights and fares, but also the history of on-time performance of a particular flight, or the likelihood of bad weather on a given week. Computers Dial for Help

The International Business Machines Corporation already uses a system in which its larger computers, when suffering some internal distress, can automatically dial a telephone number that puts it in touch with the company's service engineers. The engineers are usually given enough guidance about the problem on portable, hand-held terminals that they know which spare parts to bring along.

But so far, such systems seem to create as much work for humans as they eliminate. ''They basically act as repair manual, and tell you what buttons to push,'' Mr. Turpin said.

And the prospects that such systems will be able to talk to computer neophytes any time soon still seem remote. The chief blockade is what computer experts call the ''natural language'' barrier, the ability of a computer to speak to the end-user about a complex topic - more complex than how much to withdraw from a bank account - without using a human ''translator.'' An Airline Problem

That problem still bedevils the airline industry, which has yet to find a convenient way to have computers convey a vast amount of flight options to users without the intercession of a travel agent or reservations clerk. ''If you need someone to interpret the results of what the computer says,'' said one frustrated airline executive recently, ''then you have defeated the purpose of the machine: to save the labor of doing the job.''